Health Literacy Rates in a Population of Patients with Rheumatoid Arthritis in Southwestern Ontario
Bibliographic record
Abstract
OBJECTIVE: To determine the rate of low health literacy in the rheumatoid arthritis (RA) population in southwestern Ontario. METHODS: For the study, 432 patients with RA were contacted, and 311 completed the assessment. The health literacy levels of the participants were estimated using 4 assessment tools administered in the following order: the Single Item Literacy Screener (SILS), the Medical Term Recognition Test (METER), the Rapid Estimate of Adult Literacy in Medicine (REALM), and the Shortened Test of Functional Health Literacy in Adults (STOFHLA). RESULTS: The rates of low literacy as estimated by STOFHLA, REALM, METER, and SILS were 14.5%, 14.8%, 14.1%, and 18.6%, respectively. All 4 assessment tools were statistically significantly correlated. STOFHLA, REALM, and METER were strongly correlated with each other (r = 0.59-0.79), while SILS only demonstrated moderate correlations with the other assessment tools (r = 0.33-0.45). Multiple linear regression and binary logistic regression analyses revealed that low levels of education and a lack of daily reading activity were common predictors of low health literacy. Using a non-English primary language at home was found to be a strong predictor of low health literacy in STOFHLA, REALM, and METER. Male sex was found to be a significant predictor of poor performance in REALM and METER, but not STOFHLA. CONCLUSION: Low health literacy is an important issue in the southwestern Ontario RA population. About 1 in 7 patients with RA may not have the necessary skills to become involved in making decisions regarding their personal health. Rheumatologists should be aware of the low health literacy levels of patients with RA and should consider identifying patients at risk of low health literacy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".